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Azure Data Engineer

Gurugram, Haryana, India

Experience
6–8 years
Employment
Full-time
Work mode
Onsite
Salary
₹10 L–15 L / year
Deadline
Apply by 1 Nov 2026
Posted
2025-02-28

Required skills

SkillExperienceLevel
Azure5+ yearsIntermediate
Azure DataBricksNot specifiedNot specified
PySparkNot specifiedNot specified
Azure Data FactoryNot specifiedNot specified

About the role

Azure Data Engineer

Candidate Skills and Qualifications:

• Experience: 6 to 8+ years as an Azure Data Engineer, with a strong focus on Azure Databricks and PySpark.

• Data Pipeline Development: Proven expertise in designing, developing and maintaining complex data pipelines on Azure platforms.

• Data Migration: Experience in migrating data from on-premises and cloud-based systems to Azure, ensuring seamless data integration.

• Data Modeling: Proficient in dimensional data modeling and designing data warehouses for efficient data storage and retrieval.

• Azure Services Proficiency: Hands-on experience with Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Data Lake and Azure SQL Database.

• Programming Skills: Strong proficiency in Python and SQL; familiarity with Scala is advantageous.

• Big Data Frameworks: Experience with big data frameworks, particularly Apache Spark, for large-scale data processing.

• Version Control and CI/CD: Familiarity with Azure DevOps, Git and implementing CI/CD pipelines for automated deployments.

• Security Practices: Knowledge of Azure security practices, including Azure Active Directory (AAD), Key Vault and Role-Based Access Control (RBAC).


Role & Responsibilities

• Data Pipeline Management: Design, develop and maintain complex data pipelines on Azure to support business requirements.

• Data Migration: Migrate data from on-premises and cloud-based systems to Azure platforms, ensuring data integrity and security.• Data Modeling and Warehousing: Implement and optimize dimensional data models and data warehouses for scalable and efficient data processing.

• Azure Services Utilization: Work extensively with Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Data Lake and Azure SQL Database to manage and process large datasets.

• Programming: Write efficient and scalable code using Python and SQL to support data engineering workflows.

• ETL/ELT Processes: Develop and maintain ETL/ELT processes, leveraging big data frameworks such as Apache Spark for large-scale data processing.

• CI/CD Implementation: Set up and manage Azure DevOps pipelines, Git repositories and CI/CD processes for seamless deployment and automation.

• Security Compliance: Ensure compliance with Azure security best practices, including implementing AAD, Key Vault and RBAC for secure data access.

• Collaboration: Collaborate with cross-functional teams to identify, develop and implement data solutions to meet organizational needs.

• Monitoring and Troubleshooting: Monitor and troubleshoot data workflows, ensuring high performance and reliability.


Preferred Candidate Profile

• Experience: Minimum of 5 years as an Azure Data Engineer.

• Certifications: At least one relevant certification, such as the Microsoft


Certified: Azure Data Engineer Associate.

• Azure Data Services: In-depth knowledge of Azure data services, including Data Factory, Databricks, Synapse Analytics, Data Lake and SQL Database.

• Programming Skills: Strong programming skills in Python and SQL; familiarity with Scala is a plus.

• Data Modeling and ETL: Expertise in data modeling and ETL/ELT processes.• Big Data Frameworks: Hands-on experience with big data frameworks, such as Apache Spark.

• Version Control and CI/CD: Familiarity with Azure DevOps, Git and CI/CD implementation.

• Security Practices: Understanding of Azure security practices, including Azure Active Directory (AAD), Key Vault and Role-Based Access Control (RBAC).

• Analytical Skills: Strong analytical and problem-solving abilities.

• Communication: Excellent communication and teamwork skills.

• Adaptability: Ability to adapt to changing business needs and work effectively in an offshore setup.

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